Databricks cheat sheet
Your product needs a data platform that can: - answer-Address all data, analytics, and
AI use cases in one platform
-Fully manage your data infrastruture to maximiaze your speed to product
-Prepare your product for growth with cost effective scalbility and performance
-Offer infrastructure flexibility with open source and multi cloud
I saw you're involved with the data strategy at {{company}}, and I wanted to reach out,
as I work with data teams at startups based in your area. We often come across teams
who are growing quickly and are focused on - answerEnsuring faster and more reliable
delivery of data
Building a future proofed data platform
Increasing team productivity and collaboration
Reducing operational costs.
Lakehouse pitch - answerSHORT VERSION: Historically we have seen organizations
have a couple different stacks for processing data, one being a dw stack. These were
great for analyzing structured data that primarily served backwards looking analytics.
Data warehouses have their own proprietary formats that force users to invest in high
compute and storage to handle their peak user traffic. With the exponential growth
many companies have seen in their data, this design can become extremely expensive
and it only serves one type of analytical workload. So as companies wanted to
implement more advanced analytics they built out data lakes to help analyze all of these
other forms of semi structured data and implement future looking analytical workloads
like ML and AI workloads. The data lake takes advantage of cheap storage and being
able to handle all types of data. However it has its own drawbacks because they can
quickly become what is known as a data swamp - a poorly maintained data lake that is
difficult to navigate and query
So we have created the Lake House which combines the best of both worlds between a
data warehouse and data lake. We created a platform Where we make a data lake
more organized and reliable to allow BI workloads seen in DW, as well as future looking
workloads like ML and AI seen in data lakes all in one spot. It's and open, simple and
collaborative platform that supports all of your analytical workloads and all types of data.
The data lake takes advantage of what - answercheap storage and ability to handle all
types of Data
Draw back of a data lake; - answercan become known as a data swamp, which is
difficult to navigate and query
Lake house combines what - answerData warehouse and dtaa lake
Your product needs a data platform that can: - answer-Address all data, analytics, and
AI use cases in one platform
-Fully manage your data infrastruture to maximiaze your speed to product
-Prepare your product for growth with cost effective scalbility and performance
-Offer infrastructure flexibility with open source and multi cloud
I saw you're involved with the data strategy at {{company}}, and I wanted to reach out,
as I work with data teams at startups based in your area. We often come across teams
who are growing quickly and are focused on - answerEnsuring faster and more reliable
delivery of data
Building a future proofed data platform
Increasing team productivity and collaboration
Reducing operational costs.
Lakehouse pitch - answerSHORT VERSION: Historically we have seen organizations
have a couple different stacks for processing data, one being a dw stack. These were
great for analyzing structured data that primarily served backwards looking analytics.
Data warehouses have their own proprietary formats that force users to invest in high
compute and storage to handle their peak user traffic. With the exponential growth
many companies have seen in their data, this design can become extremely expensive
and it only serves one type of analytical workload. So as companies wanted to
implement more advanced analytics they built out data lakes to help analyze all of these
other forms of semi structured data and implement future looking analytical workloads
like ML and AI workloads. The data lake takes advantage of cheap storage and being
able to handle all types of data. However it has its own drawbacks because they can
quickly become what is known as a data swamp - a poorly maintained data lake that is
difficult to navigate and query
So we have created the Lake House which combines the best of both worlds between a
data warehouse and data lake. We created a platform Where we make a data lake
more organized and reliable to allow BI workloads seen in DW, as well as future looking
workloads like ML and AI seen in data lakes all in one spot. It's and open, simple and
collaborative platform that supports all of your analytical workloads and all types of data.
The data lake takes advantage of what - answercheap storage and ability to handle all
types of Data
Draw back of a data lake; - answercan become known as a data swamp, which is
difficult to navigate and query
Lake house combines what - answerData warehouse and dtaa lake
